{"title":"Mathematical relationships between control group variability and assay quality metrics","authors":"Andrew Lim","doi":"10.1016/j.slasd.2023.02.003","DOIUrl":null,"url":null,"abstract":"<div><p>Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z’-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z’-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.</p></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2472555223000163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z’-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z’-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.